Doctors, Drugs, and Death: Separating Supply and Demand Factors in the Opioid Crisis
Bibliographic record
Abstract
Dramatic reversals in mortality trends have been identified in the United States and Canada since 1997, in part driven by rising opioid-related deaths. Two schools of thought have emerged to explain these statistics: demand and supply side forces. The supply-side channel argues that these deaths are a product of the increasing availability of legally prescribed opioids. The demand-side channel argues that growing income inequality creates a mental health burden, which can manifest in individuals as suicidality and substance abuse. This thesis investigates precisely the role of these two channels on health consequences and mortality in the United States and Canada. The first chapter focuses on supply-side forces in the opioid epidemic, particularly through the role of pharmaceutical firm promotion and physician prescribing in the United States. In order to disentangle the supply behaviour of doctors from the demand behaviour of patients, it leverages the staggered introduction of Medicaid expansion across states to exogenously shift opioid supply. The results suggest that pharmaceutical firms respond strategically to this policy by increasing their promotional targeting of physicians. These promotions appear to be effective in changing physicians prescribing behaviour, even during a period of increased awareness of the dangers of these drugs. While not persistent, this is associated with a small increase in opioid-related mortality. The second chapter focuses on the role of absolute and relative income effects on hospitalizations in Canada. To identify the effect of a change of one's position in the income distribution, it exploits the heterogeneous effects of exogenous movements in the price of oil on the distribution of local income. Using hospitalization records linked to census data, it demonstrates that non-oil workers who have many neighbors in the oil industry are more likely to be hospitalized after oil prices rise (and their relative income falls). These results shed new light on mechanisms through which income inequality might affect people's well-being. Overall, these two chapters highlight the complex interrelationships between supply and demand side forces in the opioid epidemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".